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epiG: statistical inference and profiling of DNA methylation from whole-genome bisulfite sequencing data

The study of epigenetic heterogeneity at the level of individual cells and in whole populations is the key to understanding cellular differentiation, organismal development, and the evolution of cancer. We develop a statistical method, epiG, to infer and differentiate between different epi-allelic h...

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Detalles Bibliográficos
Autores principales: Vincent, Martin, Mundbjerg, Kamilla, Skou Pedersen, Jakob, Liang, Gangning, Jones, Peter A., Ørntoft, Torben Falck, Dalsgaard Sørensen, Karina, Wiuf, Carsten
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5320668/
https://www.ncbi.nlm.nih.gov/pubmed/28222791
http://dx.doi.org/10.1186/s13059-017-1168-4
Descripción
Sumario:The study of epigenetic heterogeneity at the level of individual cells and in whole populations is the key to understanding cellular differentiation, organismal development, and the evolution of cancer. We develop a statistical method, epiG, to infer and differentiate between different epi-allelic haplotypes, annotated with CpG methylation status and DNA polymorphisms, from whole-genome bisulfite sequencing data, and nucleosome occupancy from NOMe-seq data. We demonstrate the capabilities of the method by inferring allele-specific methylation and nucleosome occupancy in cell lines, and colon and tumor samples, and by benchmarking the method against independent experimental data. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13059-017-1168-4) contains supplementary material, which is available to authorized users.